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ChatterBot is a machine-learning based conversational dialog engine that makes it possible to generate responses based on collections of known conversations. The language independent design of ChatterBot allows it to be trained to speak any language. Additionally, the machine-learning nature of ChatterBot allows an agent instance to improve it's knowledge of possible responses as it interacts with humans and other sources of informative data.
An example of typical input would be something like this:
user: Good morning! How are you doing?
bot: I am doing very well, thank you for asking.
user: You're welcome.
bot: Do you like hats?
An untrained instance of ChatterBot starts off with no knowledge of how to communicate. Each time a user enters a statement, the library saves the text that they entered and the text that the statement was in response to. As ChatterBot receives more input the number of responses that it can reply and the accuracy of each response in relation to the input statement increase. The program selects the closest matching response by searching for the closest matching known statement that matches the input, it then returns the most likely response to that statement based on how frequently each response is issued by the people the bot communicates with.
from chatterbot import ChatBot
chatbot = ChatBot("Ron Obvious")
# Train based on the english corpus
chatbot.train("chatterbot.corpus.english")
# Get a response to an input statement
chatbot.get_response("Hello, how are you today?")ChatterBot comes with a data utility module that can be used to train chat bots. Lists of statements representing conversations can also be used for training. More information is available in the training documentation.